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I'm an undergraduate student at the California Institute of Technology who studies quantum information, high energy physics, and the intersection of machine learning and science.


  • I'm currently developing error mitigation methods for quantum circuits at the Institute of Quantum Information and Matter at Caltech, using machine learning to create an intelligent compiler for noisy intermediate-scale quantum devices.
  • I proposed and developed energy correction and clustering components in an end-to-end deep learning pipeline for neutrino experiments (DUNE and MicroBooNE) with the DeepLearnPhysics collaboration at Stanford, using semantic segmentation and graph neural networks.
  • I demonstrated how deep learning can find particle tracks in the Large Hadron Collider at CERN under the HEP.Trkx project using graph neural networks.
  • I proposed a new quantum machine learning algorithm for Higgs boson classification on the D-Wave quantum computer, as well as quantum annealing for particle tracking at the Large Hadron Collider.
  • I simulated black hole collisions and implemented Bayesian methods for detecting the astrophysical gravitational wave background at LIGO.
  • I developed genetic algorithm optimization for neutron imaging analysis of scintillating crystals at the Berkeley Space Sciences Lab, publishing our results in a peer-reviewed paper.


  • "Novel machine learning algorithms for quantum annealing with applications in high energy physics.” Quantum Techniques in Machine Learning, Korea Advanced Institute of Science and Technology (KAIST), October 2019.
  • "Machine learning applications of quantum annealing in high energy physics.” AI-at-SLAC Seminar, Stanford Linear Accelerator Center, August 2019. (Abstract here.)


  • A. Zlokapa, A. Mott, J. Job, J.-R. Vlimant, D. Lidar and M. Spiropulu, “Quantum adiabatic machine learning with zooming.” arXiv:1908.04480 [quant-ph], 2019. (To be published. Available here.)
  • A. Zlokapa, A. Anand, J.-R. Vlimant, J. Duarte, J. Job, D. Lidar and M. Spiropulu, “Charged particle tracking with quantum annealing-inspired optimization.” arXiv:1908.04475 [quant-ph], 2019. (To be published. Available here.)
  • A. Zlokapa, K. Terao, H. Tanaka, and M. Spiropulu, “Machine learning methods for event reconstruction with liquid argon time projection chamber data.” DUNE Document, 2019. (Under submission.)
  • A. Zlokapa, J.-R. Vlimant, and M. Spiropulu, “Optimizing Monte Carlo event generation using evolutionary computing techniques.” CERN CMS analysis note, 2019. (Under submission.)
  • J.-R. Vlimant, F. Pantaleo, M. Pierini, V. Loncar, S. Vallecorsa, D. Anderson, T. Nguyen, and A. Zlokapa, "Training Generative Adversarial Models over Distributed Computing Systems.” Proceedings of the 23rd International Conference on Computing in High Energy and Nuclear Physics, 2018. (Accepted.)
  • A. Tremsin, D. Perrodin, A. Losko, S. Vogel, T. Shinohara, K. Oikawa, J. Peterson, C. Zhang, J. Derby, A. Zlokapa, G. Bizarii and E. Bourret, "In-Situ Observation of Phase Separation During Growth of Cs2LiLaBr6:Ce Crystals Using Energy-Resolved Neutron Imaging." Crystal Growth & Design, 2017, 17 (12), 6372-6381.


  • X. Ju, A. Zlokapa, S. Farrell, J.-R. Vlimant, L. Gray, P. Calafiura, and M. Spiropulu, “Graph Neural Networks for Particle Reconstruction in High Energy Physics Detectors.” 33rd Annual Conference on Neural Information Processing Systems, Machine Learning for Physical Sciences Workshops, December 2019.
  • A. Zlokapa and A. Gheorghiu, “A deep learning approach to noise prediction and circuit optimization for near-term quantum devices.” IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis, November 2019. (1st place, ACM Student Research Competition.)


My CV contains the full list, but here are some highlights:

  • 1st place, ACM Student Research Competition, Supercomputing 2019
  • 1st place ($100,000), Citadel Data Open International Championship (and 1st in West Coast)
  • Hacktech (MLH@Caltech): Best Machine Learning Hack, Best Hardware Hack, Best IoT Hack
  • 2nd place, Intel International Science & Engineering Fair
  • Perfect SAT Score (top 0.02% nationally)
  • National Merit Scholar
  • US Presidential Scholar Candidate
  • Minor Planet 34134 Zlokapa (MIT Lincoln Lab)
  • American Invitational Mathematics Exam qualifier


I founded and am president of the Caltech Data Science Organization. Within the first month, we raised >$10,000 of corporate sponsorships and I led workshops that won members >$22,000 of prizes. In the club and my free time, I enjoy working on projects including machine learning tools to fight malaria, a virtual reality prototype for safer AI-powered firearms, supersonic rocket simulations for the FAR-MARS Prize, and deep learning for music composition.